Multimodal RAG: Techniques, Challenges, and Scaling the Future of AI

Overview

The session focuses on the implementation paths and future prospects of multimodal Retrieval‑Augmented Generation (RAG), aiming to build highly integrated systems that seamlessly fuse text, images, and other media for richer information interaction.

Core Topics

Based on semantic extraction

Based on Visual Language Model (VLM)

How to scale VLM‑based multimodal RAG

Technical route selection

Q&A

1. Semantic‑Extraction Multimodal RAG

Traditional multimodal document processing starts with image recognition (OCR) to extract text, tables, and figures, then parses each object into text for downstream retrieval and analysis.

2. Transformer‑Based Multimodal RAG

Modern deep‑learning models, especially Transformers, encode the entire document with an encoder and decode it into readable text. Compared with the OCR‑centric approach, this method captures contextual dependencies more effectively, improving coherence and consistency.

3. Visual Language Model (VLM) Multimodal RAG

VLMs directly process raw multimodal inputs (documents, images, videos) into patch embeddings, which are transformed into vectors. Multi‑vector (tensor) representations are used to reduce information loss and enhance retrieval and generation capabilities.

Technical Paths for Multimodal RAG

Traditional object recognition and parsing ("carving" route)

Transformer architecture

Visual Language Model

4. "Carving" Route Details

This route performs document structure recognition, separating paragraphs, tables, and charts. OCR transcribes text, while specialized models parse charts. Although thorough, it is time‑consuming and less automated, especially for large datasets.

5. Scaling VLM‑Based Multimodal RAG

Scaling challenges arise from large tensor sizes (e.g., 1024 vectors per document). To mitigate complexity, tensors are binarized and used for both initial retrieval and re‑ranking, achieving comparable accuracy with reduced computation.

6. Infinity Database Integration

Infinity provides indexes for structured data, dense vectors, sparse vectors, tensors, and full‑text search, enabling fused retrieval. Combining multiple search types (BM25, dense, sparse, tensor) improves accuracy, with tensor re‑ranking delivering significant gains.

7. Choosing a Technical Route

VLM‑based approaches excel for documents rich in abstract images, while traditional methods are better for structured content. Both routes will coexist, with OCR and VLM complementing each other, and tensor‑based delayed interaction becoming the standard for future multimodal RAG.

Q&A Highlights

Q1: How to handle the larger state space of multimodal data compared to natural language? A1: Mapping charts to Excel is ideal but impractical due to conversion difficulty and visual quality loss.

Q2: Why choose tensors over vectors? A2: Vectors have fixed dimensions, whereas tensors can flexibly represent variable‑length data, which vector databases cannot handle directly.

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